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PhysioEx

Python Version PyPI Version

PhysioEx ( Physiological Signal Explainer ) is a versatile python library tailored for building, training, and explaining deep learning models for physiological signal analysis.

The main purpose of the library is to propose a standard and fast methodology to train and evalutate state-of-the-art deep learning architectures for physiological signal analysis, to shift the attention from the architecture building task to the explainability task.

With PhysioEx you can simulate a state-of-the-art experiment just running the train, test_model and finetune commands; evaluating and saving the trained model; and start focusing on the explainability task!

Supported deep learning architectures

  • Chambon2018 model for sleep stage classification ( raw time series as input).
  • TinySleepNet model for sleep stage classification (raw time series as input).
  • SeqSleepNet model for sleep stage classification (time-frequency images as input).
  • SleepTransformer model for sleep stage classification (time-frequency images as input).

Supported datasets

Publicly Available:

For these datasets PhysioEx will take also to download the data with the preprocess command.

  • Sleep-EDF(78), The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers.
  • HMC (Haaglanden Medisch Centrum), is a collection of 151 whole-night PSG recordings from 85 men and 66 women, gathered at the Haaglanden Medisch Centrum sleep center. The PSG data includes 4 EEG channels (F4/M1, C4/M1, O2/M1, and C3/M2), two EOG channels (E1/M2 and E2/M2), and one bipolar chin EMG, with all signals sampled at 256 Hz.
  • DCSM (Danish Center for Sleep Medicine), is a collection of 255 randomly selected and fully anonymized overnight lab-based PSG recordings from patients seeking diagnosis for non-specific sleep-related disorders at the DCSM. The PSG setup included EEG, EOG, and EMG channels, all sampled at 256 Hz.

NSSR Datasets

These datasets can be easily get from the NSSR archive. Once downloaded in your data_folder, place them into the folder data_folder/dataset_name/ directory with name dataset_raw. Then you can run the preprocess -d dataset_name -df data_folder command to make the data readable by PhysioEx.

  • SHHS (Sleep Heart Health Study), is a multi-center cohort study designed to investigate the cardiovascular and other consequences of sleep-disordered breathing. At visit 1, it included 5,793 participants aged 40 years or older. PSG recordings were typically conducted in the subjects' homes by trained and certified technicians. The recording montage included C3/A2 and C4/A1 EEGs sampled at 125 Hz, right and left EOGs sampled at 50 Hz, and a bipolar submental EMG sampled at 125 Hz.
  • MESA (Multi-Ethnic Study of Atherosclerosis), is a multi-center prospective study of 2.237 ethnically diverse men and women aged 45-84 from six communities in the United States. PSGs recordings were obtained using in-home settings including central C4-M1 EEG, bilateral EOG and chin EMG sampled at 256Hz. PSGs were scored by one of 3 MESA certified, registered polysomnologists.
  • MrOS (The Osteoporotic Fractures in Men Study), is a multicenter study comprising 2,911 PSG recordings from men aged 65 years or older, enrolled at six clinical centers. PSG recordings were conducted in home settings and included C3/A2 and C4/A1 EEGs, chin EMG, and left-right EOG, all sampled at 256 Hz.
  • WSC (The Wisconsin Sleep Cohort) A longitudinal study of the causes, consequences, and natural history of sleep disorders using overnight in-laboratory sleep recordings gathered at the University of Wisconsin, United States, with a baseline sample of 1,500 subjects assessed at four-year intervals. The study consists of multiple visits with overnight PSG data acquisition. PSG recordings included C3/M2 EEG, EMG, and left-right EOG, all sampled at 200 Hz.

Others

  • MASS (Montreal Archive of Sleep Studies), is an open-access collaborative database containing laboratory-based PSG recordings. It includes 200 complete nights recorded from 97 men and 103 women, aged 18 to 76 years. All recordings have a sampling frequency of 256 Hz and feature an EEG montage of 4–20 channels, along with standard EOG and EMG

Installation guidelines

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$ conda create -n physioex python==3.10
$ conda activate physioex
$ conda install pip
$ pip install --upgrade pip  # On Windows, use `venv\Scripts\activate`

  1. Clone the Repository: 

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    $ git clone https://github.com/guidogagl/physioex.git
$ cd physioex

  2. Install Dependencies and Package in Development Mode 

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    $ pip install -e .

Install via pip

  1. Install PhysioEx from PyPI: 
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    $ pip install physioex

Install via pip

  1. Install PhysioEx from PyPI: 
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    $ pip install physioex

Note: the github version of the library is kept updated weekly, the PiPy version may be outdated depending on the last commit of the github version. We recommend to use the github version if possible.

Cite Us!

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@article{10.1088/1361-6579/adaf73,
    author={Gagliardi, Guido and Alfeo, Luca and Cimino, Mario G C A and Valenza, Gaetano and De Vos, Maarten},
    title={PhysioEx, a new Python library for explainable sleep staging through deep learning},
    journal={Physiological Measurement},
    url={http://iopscience.iop.org/article/10.1088/1361-6579/adaf73},
    year={2025},
}